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Record W1484362193 · doi:10.1002/jmri.24656

Spinal cord response to stepwise and block presentation of thermal stimuli: A functional MRI study

2014· article· en· W1484362193 on OpenAlexaff
Rachael L. Bosma, Patrick W. Stroman

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2014
Typearticle
Languageen
FieldMedicine
TopicInfrared Thermography in Medicine
Canadian institutionsQueen's University
Fundersnot available
KeywordsBrainstemSpinal cordFunctional magnetic resonance imagingSensory systemMagnetic resonance imagingNeuroscienceCordDorsumMedicineAnatomyPsychologyRadiologySurgery

Abstract

fetched live from OpenAlex

PURPOSE: To examine the characteristics of the spinal cord and brainstem blood oxygenation level-dependent (BOLD) responses to peripheral stimulation in which the temperature is raised in a stepwise fashion, in order to enhance receptor responses, compared to a block design. MATERIALS AND METHODS: Functional magnetic resonance imaging (fMRI) studies of the spinal cord and brainstem were carried out in 14 healthy volunteers at 3T. Thermal sensory stimuli were applied to the right hand in a block-design paradigm, and in a stepwise paradigm to the same peak temperature. Data were analyzed by means of a general linear model, region of interest analyses, and by structural equation modeling. RESULTS: Results demonstrated BOLD responses in a number of consistent regions between the two paradigms as well as significant differences (P < .001) in the locations and magnitudes of some responses. Specifically, the BOLD response in the dorsal horn was significantly higher in the stepwise compared to the block condition (P < .001). However, more significant connections (T >2) between regions were observed in the block condition. CONCLUSION: Results from this study demonstrate the means to design thermal sensory paradigms to probe components of sensory processing in the brainstem and spinal cord.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.302
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations26
Published2014
Admission routes1
Has abstractyes

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